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import torch |
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import torch.nn as nn |
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from torch.autograd import Function |
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from torch.autograd.function import once_differentiable |
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from torch.nn.modules.utils import _pair |
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from ..utils import ext_loader |
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ext_module = ext_loader.load_ext('_ext', |
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['roi_pool_forward', 'roi_pool_backward']) |
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class RoIPoolFunction(Function): |
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@staticmethod |
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def symbolic(g, input, rois, output_size, spatial_scale): |
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return g.op( |
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'MaxRoiPool', |
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input, |
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rois, |
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pooled_shape_i=output_size, |
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spatial_scale_f=spatial_scale) |
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@staticmethod |
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def forward(ctx, input, rois, output_size, spatial_scale=1.0): |
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ctx.output_size = _pair(output_size) |
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ctx.spatial_scale = spatial_scale |
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ctx.input_shape = input.size() |
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assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!' |
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output_shape = (rois.size(0), input.size(1), ctx.output_size[0], |
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ctx.output_size[1]) |
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output = input.new_zeros(output_shape) |
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argmax = input.new_zeros(output_shape, dtype=torch.int) |
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ext_module.roi_pool_forward( |
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input, |
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rois, |
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output, |
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argmax, |
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pooled_height=ctx.output_size[0], |
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pooled_width=ctx.output_size[1], |
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spatial_scale=ctx.spatial_scale) |
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ctx.save_for_backward(rois, argmax) |
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return output |
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@staticmethod |
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@once_differentiable |
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def backward(ctx, grad_output): |
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rois, argmax = ctx.saved_tensors |
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grad_input = grad_output.new_zeros(ctx.input_shape) |
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ext_module.roi_pool_backward( |
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grad_output, |
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rois, |
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argmax, |
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grad_input, |
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pooled_height=ctx.output_size[0], |
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pooled_width=ctx.output_size[1], |
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spatial_scale=ctx.spatial_scale) |
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return grad_input, None, None, None |
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roi_pool = RoIPoolFunction.apply |
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class RoIPool(nn.Module): |
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def __init__(self, output_size, spatial_scale=1.0): |
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super(RoIPool, self).__init__() |
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self.output_size = _pair(output_size) |
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self.spatial_scale = float(spatial_scale) |
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def forward(self, input, rois): |
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return roi_pool(input, rois, self.output_size, self.spatial_scale) |
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def __repr__(self): |
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s = self.__class__.__name__ |
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s += f'(output_size={self.output_size}, ' |
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s += f'spatial_scale={self.spatial_scale})' |
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return s |
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